{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Import IRIS dataset\n",
    "iris=datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data=pd.DataFrame(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_data.columns=iris.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
       "0                  5.1               3.5                1.4               0.2\n",
       "1                  4.9               3.0                1.4               0.2\n",
       "2                  4.7               3.2                1.3               0.2\n",
       "3                  4.6               3.1                1.5               0.2\n",
       "4                  5.0               3.6                1.4               0.2\n",
       "..                 ...               ...                ...               ...\n",
       "145                6.7               3.0                5.2               2.3\n",
       "146                6.3               2.5                5.0               1.9\n",
       "147                6.5               3.0                5.2               2.0\n",
       "148                6.2               3.4                5.4               2.3\n",
       "149                5.9               3.0                5.1               1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Standardization\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler=StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_scaled=scaler.fit_transform(iris_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        -1.31544430e+00],\n",
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       "        -1.31544430e+00],\n",
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       "         2.64141916e-01],\n",
       "       [ 1.03800476e+00, -1.31979479e-01,  7.05920842e-01,\n",
       "         6.59038469e-01],\n",
       "       [ 1.89829664e-01, -3.62176246e-01,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
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       "        -2.62386821e-01],\n",
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       "        -1.30754636e-01],\n",
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       "        -2.62386821e-01],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  8.07091462e-02,\n",
       "         8.77547895e-04],\n",
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       "         5.27406285e-01],\n",
       "       [-5.37177559e-01, -1.31979479e-01,  4.21733708e-01,\n",
       "         3.95774101e-01],\n",
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       "         5.27406285e-01],\n",
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       "         3.95774101e-01],\n",
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       "         1.32509732e-01],\n",
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       "         1.32509732e-01],\n",
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       "         1.32509732e-01],\n",
       "       [-4.16009689e-01, -1.05276654e+00,  3.64896281e-01,\n",
       "         8.77547895e-04],\n",
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       "         2.64141916e-01],\n",
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       "         1.32509732e-01],\n",
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       "         8.77547895e-04],\n",
       "       [-1.73673948e-01, -3.62176246e-01,  2.51221427e-01,\n",
       "         1.32509732e-01],\n",
       "       [ 4.32165405e-01, -3.62176246e-01,  3.08058854e-01,\n",
       "         1.32509732e-01],\n",
       "       [-9.00681170e-01, -1.28296331e+00, -4.30827696e-01,\n",
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       "         1.71209594e+00],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.52267624e+00, -1.31979479e-01,  1.21745768e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 5.53333275e-01, -3.62176246e-01,  1.04694540e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  1.16062026e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 2.12851559e+00, -1.31979479e-01,  1.61531967e+00,\n",
       "         1.18556721e+00],\n",
       "       [-1.14301691e+00, -1.28296331e+00,  4.21733708e-01,\n",
       "         6.59038469e-01],\n",
       "       [ 1.76501198e+00, -3.62176246e-01,  1.44480739e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 1.03800476e+00, -1.28296331e+00,  1.16062026e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 1.64384411e+00,  1.24920112e+00,  1.33113254e+00,\n",
       "         1.71209594e+00],\n",
       "       [ 7.95669016e-01,  3.28414053e-01,  7.62758269e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 6.74501145e-01, -8.22569778e-01,  8.76433123e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.15917263e+00, -1.31979479e-01,  9.90107977e-01,\n",
       "         1.18556721e+00],\n",
       "       [-1.73673948e-01, -1.28296331e+00,  7.05920842e-01,\n",
       "         1.05393502e+00],\n",
       "       [-5.25060772e-02, -5.92373012e-01,  7.62758269e-01,\n",
       "         1.58046376e+00],\n",
       "       [ 6.74501145e-01,  3.28414053e-01,  8.76433123e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  9.90107977e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 2.24968346e+00,  1.70959465e+00,  1.67215710e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 2.24968346e+00, -1.05276654e+00,  1.78583195e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 1.89829664e-01, -1.97355361e+00,  7.05920842e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 1.28034050e+00,  3.28414053e-01,  1.10378283e+00,\n",
       "         1.44883158e+00],\n",
       "       [-2.94841818e-01, -5.92373012e-01,  6.49083415e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 2.24968346e+00, -5.92373012e-01,  1.67215710e+00,\n",
       "         1.05393502e+00],\n",
       "       [ 5.53333275e-01, -8.22569778e-01,  6.49083415e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 1.64384411e+00,  3.28414053e-01,  1.27429511e+00,\n",
       "         7.90670654e-01],\n",
       "       [ 4.32165405e-01, -5.92373012e-01,  5.92245988e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 3.10997534e-01, -1.31979479e-01,  6.49083415e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
       "         1.18556721e+00],\n",
       "       [ 1.64384411e+00, -1.31979479e-01,  1.16062026e+00,\n",
       "         5.27406285e-01],\n",
       "       [ 1.88617985e+00, -5.92373012e-01,  1.33113254e+00,\n",
       "         9.22302838e-01],\n",
       "       [ 2.49201920e+00,  1.70959465e+00,  1.50164482e+00,\n",
       "         1.05393502e+00],\n",
       "       [ 6.74501145e-01, -5.92373012e-01,  1.04694540e+00,\n",
       "         1.31719939e+00],\n",
       "       [ 5.53333275e-01, -5.92373012e-01,  7.62758269e-01,\n",
       "         3.95774101e-01],\n",
       "       [ 3.10997534e-01, -1.05276654e+00,  1.04694540e+00,\n",
       "         2.64141916e-01],\n",
       "       [ 2.24968346e+00, -1.31979479e-01,  1.33113254e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 5.53333275e-01,  7.88807586e-01,  1.04694540e+00,\n",
       "         1.58046376e+00],\n",
       "       [ 6.74501145e-01,  9.82172869e-02,  9.90107977e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.89829664e-01, -1.31979479e-01,  5.92245988e-01,\n",
       "         7.90670654e-01],\n",
       "       [ 1.28034050e+00,  9.82172869e-02,  9.33270550e-01,\n",
       "         1.18556721e+00],\n",
       "       [ 1.03800476e+00,  9.82172869e-02,  1.04694540e+00,\n",
       "         1.58046376e+00],\n",
       "       [ 1.28034050e+00,  9.82172869e-02,  7.62758269e-01,\n",
       "         1.44883158e+00],\n",
       "       [-5.25060772e-02, -8.22569778e-01,  7.62758269e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 1.15917263e+00,  3.28414053e-01,  1.21745768e+00,\n",
       "         1.44883158e+00],\n",
       "       [ 1.03800476e+00,  5.58610819e-01,  1.10378283e+00,\n",
       "         1.71209594e+00],\n",
       "       [ 1.03800476e+00, -1.31979479e-01,  8.19595696e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 5.53333275e-01, -1.28296331e+00,  7.05920842e-01,\n",
       "         9.22302838e-01],\n",
       "       [ 7.95669016e-01, -1.31979479e-01,  8.19595696e-01,\n",
       "         1.05393502e+00],\n",
       "       [ 4.32165405e-01,  7.88807586e-01,  9.33270550e-01,\n",
       "         1.44883158e+00],\n",
       "       [ 6.86617933e-02, -1.31979479e-01,  7.62758269e-01,\n",
       "         7.90670654e-01]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_scaled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Apply the PCA\n",
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca=PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pca_scaled=pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b2ae6d5850>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Eucledian Distance')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Agglomerative Clustering\n",
    "## To construct a dendogram\n",
    "import scipy.cluster.hierarchy as sc\n",
    "##plot the dendogram\n",
    "plt.figure(figsize=(20,7))\n",
    "plt.title(\"Dendograms\")\n",
    "\n",
    "## create dendogram\n",
    "sc.dendrogram(sc.linkage(pca_scaled,method='ward'))\n",
    "plt.title('Dendogram')\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('Eucledian Distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "cluster=AgglomerativeClustering(n_clusters=2,affinity='euclidean',linkage='ward')\n",
    "cluster.fit(pca_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1b2af624a30>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(pca_scaled[:,0],pca_scaled[:,1],c=cluster.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "## silhouette score\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "silhouette_coefficients = []\n",
    "\n",
    "# Notice you start at 2 clusters for silhouette coefficient\n",
    "for k in range(2, 11):\n",
    "    agglo = AgglomerativeClustering(n_clusters=k,affinity='euclidean',linkage='ward')\n",
    "    agglo.fit(X_scaled)\n",
    "    score = silhouette_score(X_scaled, agglo.labels_)\n",
    "    silhouette_coefficients.append(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plotting silhouette score\n",
    "plt.plot(range(2,11),silhouette_coefficients)\n",
    "plt.xticks(range(2,11))\n",
    "plt.xlabel(\"Number of Cluters\")\n",
    "plt.ylabel(\"Silhoutte Coeffecient\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
